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DBMS > Apache Spark (SQL) vs. Citus vs. PostgreSQL

System Properties Comparison Apache Spark (SQL) vs. Citus vs. PostgreSQL

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonCitus  Xexclude from comparisonPostgreSQL  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL
Primary database modelRelational DBMSRelational DBMSRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.
Secondary database modelsDocument storeDocument store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.45
Rank#31  Overall
#19  Relational DBMS
Score1.98
Rank#114  Overall
#55  Relational DBMS
Score659.62
Rank#4  Overall
#4  Relational DBMS
Websitespark.apache.org/­sqlwww.citusdata.comwww.postgresql.org
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.citusdata.comwww.postgresql.org/­docs
DeveloperApache Software FoundationPostgreSQL Global Development Group infowww.postgresql.org/­developer
Initial release201420101989 info1989: Postgres, 1996: PostgreSQL
Current release3.5.0 ( 2.13), September 20238.1, December 201816.4, August 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL, commercial license also availableOpen Source infoBSD
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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PostgreSQL Flex @ STACKIT offers managed PostgreSQL Instances with adjustable CPU, RAM, storage amount and speed and several extensions available, in enterprise grade to perfectly match all application requirements. All 100% GDPR-compliant.
Implementation languageScalaCC
Server operating systemsLinux
OS X
Windows
LinuxFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyes infospecific XML type available, but no XML query functionalityyes infospecific XML-type available, but no XML query functionality.
Secondary indexesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionsyes infostandard with numerous extensions
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesJava
Python
R
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingpartitioning by range, list and (since PostgreSQL 11) by hash
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsSource-replica replication infoother methods possible by using 3rd party extensions
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlnofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)CitusPostgreSQL
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